Sentiment analysis is a widely researched area within Natural Language Processing (NLP), attracting significant interest due to the advent of automated solutions. Despite this, the task remains challenging because of the inherent complexity of languages and the subjective nature of sentiments. It is even more challenging for less-studied and less-resourced languages such as Lithuanian. Our review of existing Lithuanian NLP research reveals that traditional machine learning methods and classification algorithms have limited effectiveness for the task. In this work, we address sentiment analysis of Lithuanian five-star-based online reviews from multiple domains that we collect and clean. We apply transformer models to this task for the first time, exploring the capabilities of pre-trained multilingual Large Language Models (LLMs), specifically focusing on fine-tuning BERT and T5 models. Given the inherent difficulty of the task, the fine-tuned models perform quite well, especially when the sentiments themselves are less ambiguous: 80.74% and 89.61% testing recognition accuracy of the most popular one- and five-star reviews respectively. They significantly outperform current commercial state-of-the-art general-purpose LLM GPT-4. We openly share our fine-tuned LLMs online.

翻译：情感分析是自然语言处理（NLP）领域一个被广泛研究的课题，随着自动化解决方案的出现而备受关注。尽管如此，由于语言固有的复杂性和情感的主观性，这项任务仍然充满挑战。对于立陶宛语这类研究较少且资源匮乏的语言而言，挑战则更为严峻。我们对现有立陶宛语NLP研究的回顾表明，传统的机器学习方法和分类算法在此任务上效果有限。在本工作中，我们针对从多个领域收集并清洗的、基于五星评级的立陶宛语在线评论进行情感分析。我们首次将Transformer模型应用于此任务，探索了预训练多语言大型语言模型（LLMs）的能力，特别聚焦于对BERT和T5模型进行微调。鉴于任务本身固有的难度，微调后的模型表现相当出色，尤其是在情感本身歧义性较低的情况下：对最受欢迎的一星和五星评论的测试识别准确率分别达到80.74%和89.61%。这些模型显著优于当前商业上最先进的通用LLM GPT-4。我们已在线上公开分享我们微调后的LLMs。